In All Likelihood, Deep Belief Is Not Enough

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The archive contains Python code for training and evaluating deep belief networks (DBNs) using the estimator described in this paper, as well as sets of natural image patches (van Hateren) and handwritten digits (MNIST) for running the experiments. The parameters of the experiments were changed so as to reduce their running time while still giving similar results to the results reported in the paper. Note that an experiment can still easily take a few hours, depending on your hardware.

A minimal example for training a stack of two RBMs with data points stored in the columns of a NumPy array data: